Characteristics of Fengyun-4A Satellite Atmospheric Motion Vectors and Their Impacts on Data Assimilation

Chen, Yaodeng; Shen, Jie; Fan, Shuiyong; Meng, Dennis Desheng; Wang, Cheng

doi:10.1007/s00376-020-0080-0

Cited by 13 publications

(4 citation statements)

References 48 publications

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“…Geostationary meteorological satellites enable the retrieval of AMVs by employing image‐matching techniques that trace specific clouds on continuous satellite images, compute their displacement, and integrate this with cloud top temperatures and pressures (Velden et al., 2005). In cloudy scenarios, visible and near‐infrared radiation help trace cloud movement vectors (Chen et al., 2020; Otsuka et al., 2018). Conversely, radiances from water vapor‐sensitive bands in clear conditions help track water vapor motion vectors (Ouyed et al., 2023; Velden et al., 1997).…”

Section: Introductionmentioning

confidence: 99%

“…However, these instruments also currently lack information about upper‐level winds. Satellite‐derived wind can monitor the movement of clouds and produce atmospheric motion vectors (AMVs) focusing on cloudy areas but lack the wind profile information (Chen et al., 2020; Li et al., 2020; Lim et al., 2019). Moreover, the Aeolus satellite project has successfully obtained comprehensive wind profiles globally for the first time using the space‐based Doppler wind lidar (Li et al., 2023; Stoffelen et al., 2021; Witschas et al., 2020).…”

Section: Introductionmentioning

confidence: 99%

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Added Value of Three‐Dimensional Horizontal Winds From Geostationary Interferometric Infrared Sounder for Typhoon Forecast in a Regional NWP Model

Meng,

Tan,

et al. 2024

JGR Atmospheres

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High temporal resolution geostationary hyperspectral infrared sounders can simultaneously profile atmospheric temperature and moisture, and track moisture features to provide 3D horizontal winds in clear and partially cloudy scenarios. The thermodynamic information obtained has been integral to enhancing tropical cyclone (TC) forecasts. However, the potential benefits derived from the dynamic information provided by geostationary hyperspectral infrared sounders (GeoHIS) are yet to be fully comprehended for numerical weather prediction (NWP). With the re‐estimated observation error and multivariate hydrometeor background error covariance, the 3D horizontal winds from the Geostationary Interferometric Infrared Sounder (GIIRS) are synergistically assimilated with the hydrometeor information. The impact of the GIIRS‐derived 3D horizontal wind assimilation on Typhoon Maria (2018) and Lekima (2019) analysis and forecast are evaluated. The results show that the assimilating GIIRS‐derived 3D horizontal wind notably reduces the RMSEs of analysis and forecast, particularly in the U and V components. The improvement in accuracy of large‐scale fields substantially enhances the forecasts of typhoons' track and maximum wind speed, as well as the central sea‐level pressure. Moreover, the prediction of the spatial distribution and intensity for landfall precipitation is also improved. The detailed diagnoses of the Maria show that a more southerly subtropical high generated by the additional horizontal wind assimilation improves the rainfall spatial distribution, and the reasonable water vapor transportation caused by the improved dynamic conditions corrects the precipitation intensity. This work highlights the importance of dynamic information in TC forecasting and emphasizes the need for efficient and high‐precision 3D wind measurements to improve NWP.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Added Value of Three‐Dimensional Horizontal Winds From Geostationary Interferometric Infrared Sounder for Typhoon Forecast in a Regional NWP Model

Meng,

Tan,

et al. 2024

JGR Atmospheres

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“…Recent studies have shown AMVs from these new satellites have significantly increased observation density, decreased speed bias, and assured better quality when compared to AMVs from previous satellite generations [1][2][3][4]. In addition, various Observing System Experiments (OSEs) show positive impact of these new high-resolution satellite AMVs even in the presence of abundant satellite radiance observations in global and regional weather forecasts [1][2][3][5][6][7] and tropical cyclone and hurricane forecasts [8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Assessment of Geo-Kompsat-2A Atmospheric Motion Vector Data and Its Assimilation Impact in the GEOS Atmospheric Data Assimilation System

et al. 2022

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Korea’s second geostationary meteorological satellite, Geo-Kompsat-2A (Geostationary-Korean Multi-Purpose Satellite-2A, GK2A), was successfully launched on 4 December 2018. GK2A generates Atmospheric Motion Vectors (AMVs) every 10 min in the full disk area. This data has been disseminated via Global Telecommunication System (GTS) since 25 October 2019. This article evaluates the quality of GK2A AMVs in the Goddard Earth Observing System (GEOS) atmospheric data assimilation system (ADAS). The data show slow wind speed biases at 200–300 hPa and 600–800 hPa in the northern and southern hemispheres. These biases are caused by observation height assignment errors near jet streams. The Equivalent Blackbody Temperature (EBBT) method of GK2A tends to assign clouds at higher altitude, which mainly causes slow wind speed biases, especially in the lower atmosphere. The IR/WV intercept method of GK2A assigns clouds slightly lower in the atmospheric layers below the altitude of 400 hPa, which causes positive biases. Quality control (QC) criteria to select the most suitable GK2A AMV data for assimilation are presented based on these quality assessments. A new QC criterion utilizing height errors within the GEOS ADAS is introduced to exclude data with slow wind speed biases and large errors. GEOS forecast accuracy is slightly improved after assimilating GK2A AMVs along with other conventional, radiance, and satellite winds which include AMVs made by the Himawari-8 satellite in nearly the same observational area of GK2A. Additionally, the present work shows that GEOS forecasts can be significantly improved, especially in the tropics and southern hemisphere after assimilating GK2A data in the absence of Himawari-8 AMVs. This study demonstrates that GK2A AMV data is a valuable data source to enhance the robustness of GEOS ADAS.

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“…Cloudy skies are forecast-sensitive areas because of the large horizontal and vertical gradients in the atmospheric variables, which make the predictions under the cloudy skies very difficult (Errico et al, 2007;Geer, 2019;Geer et al, 2017Geer et al, , 2018Li et al, 2016Li et al, , 2021McNally, 2002). All-sky assimilation is expected to facilitate the forecast as clouds present because (a) mass, wind, and humidity fields can be directly improved under cloudy skies (Geer et al, 2008(Geer et al, , 2017Zhu et al, 2016), (b) observations from clouds and precipitation can provide additional information on mass, wind, and humidity (Bauer et al, 2006;Chen et al, 2020;Li et al, 2020;Sawada et al, 2020), and (c) better hydrometeor initialization can be obtained (Chen et al, 2015(Chen et al, , 2016Jones et al, 2013;Jones & Stensrud, 2015;Wu et al, 2016;Wu & Zupanski, 2017). Therefore, some numerical prediction centers (NWP) have operationalized the all-sky assimilation of satellite observation, such as the European Center for Medium-range Weather Forecasts (Bauer et al, 2010), the NOAA National Centers for Environmental Prediction (Zhu et al, 2016), and Met Office (Carminati & Migliorini, 2021), while others are ongoing to develop corresponding all-sky assimilation techniques, such as Japan Meteorological Agency (Okamoto et al, 2019), Météo-France (Duruisseau et al, 2019), and the German Weather Service (Harnisch et al, 2016).…”

mentioning

confidence: 99%

The Potential Value of Combining Assimilation of Water Vapor Radiances and Cloud Liquid/Ice Water Path From ABI on Analyses and Forecasts for Hurricane Irma (2017)

Meng

Chen

et al. 2022

JGR Atmospheres

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Characteristics of Fengyun-4A Satellite Atmospheric Motion Vectors and Their Impacts on Data Assimilation

Cited by 13 publications

References 48 publications

Added Value of Three‐Dimensional Horizontal Winds From Geostationary Interferometric Infrared Sounder for Typhoon Forecast in a Regional NWP Model

Added Value of Three‐Dimensional Horizontal Winds From Geostationary Interferometric Infrared Sounder for Typhoon Forecast in a Regional NWP Model

Assessment of Geo-Kompsat-2A Atmospheric Motion Vector Data and Its Assimilation Impact in the GEOS Atmospheric Data Assimilation System

The Potential Value of Combining Assimilation of Water Vapor Radiances and Cloud Liquid/Ice Water Path From ABI on Analyses and Forecasts for Hurricane Irma (2017)

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